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        "outputId": "43177e6c-6ad2-468b-e6fe-1c3089957456"
      },
      "source": [
        "from torchvision import datasets\n",
        "import torch\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "import numpy as np\n",
        "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
        "data_folder = '/content/' # This can be any directory\n",
        "# you want to download FMNIST to\n",
        "fmnist = datasets.FashionMNIST(data_folder, \n",
        "                               download=True,\n",
        "                               train=True)\n",
        "tr_images = fmnist.data\n",
        "tr_targets = fmnist.targets\n",
        "val_fmnist = datasets.FashionMNIST(data_folder, \n",
        "                                   download=True,\n",
        "                                   train=False)\n",
        "val_images = val_fmnist.data\n",
        "val_targets = val_fmnist.targets"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz to /content/FashionMNIST/raw/train-images-idx3-ubyte.gz\n"
          ],
          "name": "stdout"
        },
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          "metadata": {
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        {
          "output_type": "stream",
          "text": [
            "Extracting /content/FashionMNIST/raw/train-images-idx3-ubyte.gz to /content/FashionMNIST/raw\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz to /content/FashionMNIST/raw/train-labels-idx1-ubyte.gz\n"
          ],
          "name": "stdout"
        },
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        },
        {
          "output_type": "stream",
          "text": [
            "Extracting /content/FashionMNIST/raw/train-labels-idx1-ubyte.gz to /content/FashionMNIST/raw\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz to /content/FashionMNIST/raw/t10k-images-idx3-ubyte.gz\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
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        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Extracting /content/FashionMNIST/raw/t10k-images-idx3-ubyte.gz to /content/FashionMNIST/raw\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz to /content/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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          "metadata": {
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        },
        {
          "output_type": "stream",
          "text": [
            "Extracting /content/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz to /content/FashionMNIST/raw\n",
            "Processing...\n",
            "Done!\n",
            "\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/torchvision/datasets/mnist.py:480: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at  /pytorch/torch/csrc/utils/tensor_numpy.cpp:141.)\n",
            "  return torch.from_numpy(parsed.astype(m[2], copy=False)).view(*s)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zy-V6Sh9K5Ln"
      },
      "source": [
        "from imgaug import augmenters as iaa\n",
        "aug = iaa.Sequential([\n",
        "    iaa.Affine(translate_px={'x':(-10,10)},\n",
        "        mode='constant'),\n",
        "])"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GflUuLjwLAiL"
      },
      "source": [
        "class FMNISTDataset(Dataset):\n",
        "    def __init__(self, x, y, aug=None):\n",
        "        self.x, self.y = x, y\n",
        "        self.aug = aug\n",
        "    def __getitem__(self, ix):\n",
        "        x, y = self.x[ix], self.y[ix]\n",
        "        return x, y\n",
        "    def __len__(self): return len(self.x)\n",
        "\n",
        "    def collate_fn(self, batch):\n",
        "        'logic to modify a batch of images'\n",
        "        ims, classes = list(zip(*batch))\n",
        "        # transform a batch of images at once\n",
        "        if self.aug: ims=self.aug.augment_images(images=ims)\n",
        "        ims = torch.tensor(ims)[:,None,:,:].to(device)/255.\n",
        "        classes = torch.tensor(classes).to(device)\n",
        "        return ims, classes"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oP7VyqnALbyv"
      },
      "source": [
        "from torch.optim import SGD, Adam\n",
        "def get_model():\n",
        "    model = nn.Sequential(\n",
        "        nn.Conv2d(1, 64, kernel_size=3),\n",
        "        nn.MaxPool2d(2),\n",
        "        nn.ReLU(),\n",
        "        nn.Conv2d(64, 128, kernel_size=3),\n",
        "        nn.MaxPool2d(2),\n",
        "        nn.ReLU(),\n",
        "        nn.Flatten(),\n",
        "        nn.Linear(3200, 256),\n",
        "        nn.ReLU(),\n",
        "        nn.Linear(256, 10)\n",
        "    ).to(device)\n",
        "    loss_fn = nn.CrossEntropyLoss()\n",
        "    optimizer = Adam(model.parameters(), lr=1e-3)\n",
        "    return model, loss_fn, optimizer"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0PTR-xFBLcCE"
      },
      "source": [
        "def train_batch(x, y, model, opt, loss_fn):\n",
        "    model.train()\n",
        "    prediction = model(x)\n",
        "    batch_loss = loss_fn(prediction, y)\n",
        "    batch_loss.backward()\n",
        "    optimizer.step()\n",
        "    optimizer.zero_grad()\n",
        "    return batch_loss.item()"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7GzzW7kzLldB"
      },
      "source": [
        " def get_data():\n",
        "    train = FMNISTDataset(tr_images, tr_targets, aug=aug)\n",
        "    'notice the collate_fn argument'\n",
        "    trn_dl = DataLoader(train, batch_size=64,\n",
        "                collate_fn=train.collate_fn, shuffle=True)\n",
        "    val = FMNISTDataset(val_images, val_targets)\n",
        "    val_dl = DataLoader(val, batch_size=len(val_images),\n",
        "                collate_fn=val.collate_fn, shuffle=True)\n",
        "    return trn_dl, val_dl"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NDY9G3Y5Lm3T",
        "outputId": "ba7ae6ec-d7f9-4c8d-ee0f-60255b0a54aa"
      },
      "source": [
        "trn_dl, val_dl = get_data()\n",
        "model, loss_fn, optimizer = get_model()\n",
        "for epoch in range(5):\n",
        "    print(epoch)\n",
        "    for ix, batch in enumerate(iter(trn_dl)):\n",
        "        x, y = batch\n",
        "        batch_loss = train_batch(x, y, model, optimizer, loss_fn)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0\n",
            "1\n",
            "2\n",
            "3\n",
            "4\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "RYle6Lm9Lvgw",
        "outputId": "adf74a4e-b916-46bc-f5ad-cdd624efbe49"
      },
      "source": [
        "preds = []\n",
        "ix = 24300\n",
        "for px in range(-5,6):\n",
        "    img = tr_images[ix]/255.\n",
        "    img = img.view(28, 28)\n",
        "    img2 = np.roll(img, px, axis=1)\n",
        "    img3 = torch.Tensor(img2).view(-1,1,28,28).to(device)\n",
        "    np_output = model(img3).cpu().detach().numpy()\n",
        "    pred = np.exp(np_output)/np.sum(np.exp(np_output))\n",
        "    preds.append(pred)\n",
        "    plt.imshow(img2)\n",
        "    plt.title(fmnist.classes[pred[0].argmax()])\n",
        "    plt.show()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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c+/NjF4T1uc0nwvq0heeG9ZEDB8N6ZMBbwnqpw3OHSuwvuhbvy609G46UomnPLpIIhV0kEQq7SCIUdpFEKOwiiVDYRRKhsIskIp159qbmuD4WH7f9/MZFubWlnzocjt3ctCSsL26JZ5z3XxGfzvncW8ufZ2+e/Gxiv9M/1hrWD47MDOud04Zya5pnry3t2UUSobCLJEJhF0mEwi6SCIVdJBEKu0giFHaRRCQzz27N8Ty7l5hnP++e3+TWRj8VP2bObomXgx72+J9hzpresM6tcTnS7/E8+qKWeBXuJwfPC+tzWvtza0+FI6Vo2rOLJEJhF0mEwi6SCIVdJBEKu0giFHaRRCjsIokoOc9uZouBu4EFgAPr3f0rZnYT8DHg9MHcN7r7/dVqtFI+Gs+jlzL6TP7R1z84+vpw7OrOvWG9d2R2WP/Byv8I63/KH4T1yA+PvC6s/8msbWH9xGh7WF/e3pdb28L8cKwUayofqhkBPu3um81sOvBLM3sgq93q7v9QvfZEpCglw+7uB4AD2eXjZvYEkH/aFhFpSC/pNbuZLQXeCDySbVpnZlvN7A4zm/S5qJmtNbNuM+seZrCiZkWkfFMOu5mdDXwHuN7djwFfA5YDqxnf898y2Th3X+/uXe7e1UJbAS2LSDmmFHYza2E86N9y9+8CuPshdx919zHgG8CF1WtTRCpVMuxmZsDtwBPu/uUJ2xdO+LUPANuLb09EijKVd+PfBlwFbDOzLdm2G4ErzWw149NxPcB1VemwKCUOYa3Ef/asCuuf/P2fhfV9o/HLmzaLl1WuRNfZvw3rs5riw3MXtBwN68ta86fe0NRbTU3l3fiHAZuk1LBz6iLyYvoEnUgiFHaRRCjsIolQ2EUSobCLJEJhF0lEMqeSLskmm12cwPOXNl70xXjsNV+6IqzvOTQvrC+/ZTisw+Ml6vlu2vTBsP7N93wtrE9vGgjr1/7imtzaMrbk1qR42rOLJEJhF0mEwi6SCIVdJBEKu0giFHaRRCjsIokwD+aPC78xs8PAxPMqzwOeqVkDL02j9taofYF6K1eRvS1x93MmK9Q07C+6cbNud++qWwOBRu2tUfsC9VauWvWmp/EiiVDYRRJR77Cvr/PtRxq1t0btC9RbuWrSW11fs4tI7dR7zy4iNaKwiySiLmE3s0vN7DdmtsvMPlOPHvKYWY+ZbTOzLWbWXede7jCzPjPbPmHbHDN7wMx2Zt/j9Z5r29tNZtab3XdbzGxNnXpbbGY/MbNfm9njZvYX2fa63ndBXzW532r+mt3MmoEdwB8D+4FHgSvd/dc1bSSHmfUAXe5e9w9gmNkfAieAu919VbbtS8Bz7n5z9kA5291vaJDebgJO1HsZ72y1ooUTlxkH3g9cQx3vu6Cvy6nB/VaPPfuFwC533+PuQ8C3gcvq0EfDc/eHgOfO2HwZsCG7vIHx/yw1l9NbQ3D3A+6+Obt8HDi9zHhd77ugr5qoR9gXAfsm/Lyfxlrv3YEfmdkvzWxtvZuZxAJ3P5BdPggsqGczkyi5jHctnbHMeMPcd+Usf14pvUH3Yhe7+5uA9wCfyJ6uNiQffw3WSHOnU1rGu1YmWWb8d+p535W7/Hml6hH2XmDxhJ9fkW1rCO7em33vA+6j8ZaiPnR6Bd3se7RyYk010jLeky0zTgPcd/Vc/rweYX8UWGFm55tZK3AFsLEOfbyImXVmb5xgZp3Au2m8pag3Aldnl68Gvl/HXl6gUZbxzltmnDrfd3Vf/tzda/4FrGH8HfndwGfr0UNOX8uAx7Kvx+vdG3AP40/rhhl/b+NaYC6wCdgJPAjMaaDevglsA7YyHqyFdertYsafom8FtmRfa+p93wV91eR+08dlRRKhN+hEEqGwiyRCYRdJhMIukgiFXSQRCrtIIhR2kUT8P6Zn7K+KgK5NAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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33MwZbr3/UIdb9/RYhVv3puYCQF/CcbKp8aBb/8Ct5oeO7CKBUNhFAqGwiwRCYRcJhMIuEgiFXSQQCrtIIDTOPtaVlfv1tD9v+9jGWW59zreOuPUtZbNja40V/mhz203+6ZxnPJD5OHv5yGdR+63udKVb7+if6NZrx/W5dY2zi0jeKOwigVDYRQKhsIsEQmEXCYTCLhIIhV0kEBpnH+NY7o+zW8I4+/kbfu3WB77lHy8mV8QvCZ0y/9evflm7W8cDftnTbf44+qwKf/Xxd3rPd+v1ld1u/V23mh86sosEQmEXCYTCLhIIhV0kEAq7SCAUdpFAKOwigUgcZyfZCOAJAA0ADMAaM3uI5GoAXwMwNKH5HjN7Pl+NSmZswB9HTzLwvj/z+qcnLnbri2sPxNba+yf7t73wP9z6n+IP3LrnZ8c/5db/ZNJ2t941UO3W51d3uvWtmO7W82E0H6rpB3CnmW0hWQfgTZIvRLUHzOwf8teeiORKYtjN7BCAQ9HlUyR3AfBPXyIiJeecXrOTnAPgEgBvRJtWkdxGci3JEZ+TkWwm2UKyJYXerJoVkcyNOuwkxwP4CYA7zOwkgEcAzAewGINH/vtH2s/M1phZk5k1VaAqBy2LSCZGFXaSFRgM+o/M7GkAMLPDZjZgZmkAjwJYkr82RSRbiWEnSQCPAdhlZj8Ytn3msKtdB2BH7tsTkVwZzbvxnwewAsB2klujbfcAuJnkYgwOx7UCuD0vHUp2EqawZus/Wxe59W/+/muxtYMD/su6KvrLKmejafxv3PqksvipuQDQUHHCrc+r9IfeUIpDb2b2KgCOUNKYusjvEH2CTiQQCrtIIBR2kUAo7CKBUNhFAqGwiwRCp5IOHUcaVR3G/KWNZ33P3//W798UW9t/eKq77/z7U24d2JlQj7d60/Vu/cmrH3HrdWU9bv22X97q1udhq1vPBx3ZRQKhsIsEQmEXCYTCLhIIhV0kEAq7SCAUdpFA0BLGUXN6Z+QRAMPPLTwVwPsFa+DclGpvpdoXoN4ylcveZpvZtJEKBQ37R+6cbDGzpqI14CjV3kq1L0C9ZapQvelpvEggFHaRQBQ77GuKfP+eUu2tVPsC1FumCtJbUV+zi0jhFPvILiIForCLBKIoYSd5Fclfk9xL8u5i9BCHZCvJ7SS3kmwpci9rSXaS3DFsWz3JF0juib776x4XtrfVJNujx24ryWVF6q2R5Msk3ya5k+RfRNuL+tg5fRXkcSv4a3aS5QB2A/hjAG0ANgO42czeLmgjMUi2Amgys6J/AIPkHwLoAvCEmS2Ktn0fwFEzuy/6QznZzO4qkd5WA+gq9jLe0WpFM4cvMw7gWgC3ooiPndPXjSjA41aMI/sSAHvNbL+Z9QH4MYDlReij5JnZKwCOnrV5OYD10eX1GPxlKbiY3kqCmR0ysy3R5VMAhpYZL+pj5/RVEMUI+ywAB4f93IbSWu/dAPyc5Jskm4vdzAgazOxQdLkDQEMxmxlB4jLehXTWMuMl89hlsvx5tvQG3UddbmafAXA1gG9ET1dLkg2+BiulsdNRLeNdKCMsM/5bxXzsMl3+PFvFCHs7gMZhP38s2lYSzKw9+t4J4BmU3lLUh4dW0I2+J60gWDCltIz3SMuMowQeu2Iuf16MsG8GsIDkXJKVAG4CsLEIfXwEydrojROQrAVwJUpvKeqNAFZGl1cCeK6IvXxIqSzjHbfMOIr82BV9+XMzK/gXgGUYfEd+H4BvF6OHmL7mAXgr+tpZ7N4AbMDg07oUBt/buA3AFACbAOwB8CKA+hLq7UkA2wFsw2CwZhapt8sx+BR9G4Ct0deyYj92Tl8Fedz0cVmRQOgNOpFAKOwigVDYRQKhsIsEQmEXCYTCLhIIhV0kEP8Phgjsr7EuCLAAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 625
        },
        "id": "1wH7BtgCMQD5",
        "outputId": "59522b5e-0079-43fb-fbab-817111a48844"
      },
      "source": [
        "import seaborn as sns\n",
        "fig, ax = plt.subplots(1,1, figsize=(12,10))\n",
        "plt.title('Probability of each class for various translations')\n",
        "sns.heatmap(np.array(preds).reshape(11,10), annot=True, ax=ax, fmt='.2f', xticklabels=fmnist.classes, yticklabels=[str(i)+str(' pixels') for i in range(-5,6)], cmap='gray')"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe3601c36d8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IOb3uRaLMVjt"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}